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import copy |
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import numpy as np |
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from mmpose.datasets.pipelines import Compose |
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def _check_flip(origin_imgs, result_imgs): |
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"""Check if the origin_imgs are flipped correctly.""" |
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h, w, c = origin_imgs.shape |
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for i in range(h): |
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for j in range(w): |
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for k in range(c): |
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if result_imgs[i, j, k] != origin_imgs[i, w - 1 - j, k]: |
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return False |
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return True |
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def get_sample_data(): |
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ann_info = {} |
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ann_info['image_size'] = np.array([256, 256]) |
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ann_info['heatmap_size'] = np.array([64, 64, 64]) |
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ann_info['heatmap3d_depth_bound'] = 400.0 |
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ann_info['heatmap_size_root'] = 64 |
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ann_info['root_depth_bound'] = 400.0 |
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ann_info['num_joints'] = 42 |
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ann_info['joint_weights'] = np.ones((ann_info['num_joints'], 1), |
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dtype=np.float32) |
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ann_info['use_different_joint_weights'] = False |
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ann_info['flip_pairs'] = [[i, 21 + i] for i in range(21)] |
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ann_info['inference_channel'] = list(range(42)) |
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ann_info['num_output_channels'] = 42 |
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ann_info['dataset_channel'] = list(range(42)) |
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results = { |
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'image_file': 'tests/data/interhand2.6m/image69148.jpg', |
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'center': np.asarray([200, 200], dtype=np.float32), |
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'scale': 1.0, |
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'rotation': 0, |
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'joints_3d': np.zeros([42, 3], dtype=np.float32), |
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'joints_3d_visible': np.ones([42, 3], dtype=np.float32), |
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'hand_type': np.asarray([1, 0], dtype=np.float32), |
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'hand_type_valid': 1, |
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'rel_root_depth': 50.0, |
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'rel_root_valid': 1, |
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'ann_info': ann_info |
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} |
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return results |
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def test_hand_transforms(): |
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results = get_sample_data() |
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pipeline = Compose([dict(type='LoadImageFromFile')]) |
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results = pipeline(results) |
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pipeline = Compose([dict(type='HandRandomFlip', flip_prob=1)]) |
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results_flip = pipeline(copy.deepcopy(results)) |
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assert _check_flip(results['img'], results_flip['img']) |
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pipeline = Compose([dict(type='HandGenerateRelDepthTarget')]) |
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results_depth = pipeline(copy.deepcopy(results)) |
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assert results_depth['target'].shape == (1, ) |
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assert results_depth['target_weight'].shape == (1, ) |
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